Reihaneh Rabbany

Biographie

Reihaneh Rabbany est professeure adjointe à l'École d'informatique de l'Université McGill. Elle est membre du corps professoral de Mila – Institut québécois d’intelligence artificielle et titulaire d'une chaire en IA Canada-CIFAR. Elle est également membre du corps enseignant du Centre pour l’étude de la citoyenneté démocratique de McGill. Avant de se joindre à l’Université McGill, elle a été boursière postdoctorale à la School of Computer Science de l'Université Carnegie Mellon. Elle a obtenu un doctorat à l’Université de l’Alberta, au Département d'informatique. Elle dirige le laboratoire de données complexes, dont les recherches se situent à l'intersection de la science des réseaux, de l'exploration des données et de l'apprentissage automatique, et se concentrent sur l'analyse des données interconnectées du monde réel et sur les applications sociales.

Étudiants actuels

Hussein Abdallah

Postdoctorat - McGill

Taimaa Bachi

Stagiaire de recherche - McGill

Aurélien Bück-Kaeffer

Maîtrise recherche - McGill

Jacob Chmura

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Stagiaire de recherche - UdeM

Superviseur⋅e principal⋅e :

Islam Eldifrawi

Visiteur de recherche indépendant - University of Sherbrooke

Aarash Feizi

Doctorat - McGill

Co-superviseur⋅e :

Adriana Romero Soriano

Collaborateur·rice alumni - McGill

Co-superviseur⋅e :

Anne Imouza

Doctorat - McGill

Superviseur⋅e principal⋅e :

Yann Klein

Stagiaire de recherche - McGill University

Emma Kondrup

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Sitao Luan

Postdoctorat - McGill

Superviseur⋅e principal⋅e :

Guillaume Rabusseau

Shahrad Mohammadzadeh

Maîtrise recherche - McGill

Co-superviseur⋅e :

Visiteur de recherche indépendant - McGill

Collaborateur·rice alumni - McGill

Site web

Soroush Omranpour

Collaborateur·rice alumni - McGill

Co-superviseur⋅e :

Guillaume Rabusseau

Farimah Poursafaei

Collaborateur·rice alumni - McGill

Stagiaire de recherche - McGill

Sebastian Sabry

Maîtrise recherche - McGill University

Dorsaf Sallami

Stagiaire de recherche - McGill

Site web

Sneheel Sarangi

Maîtrise recherche - McGill

Maîtrise recherche - McGill

Camille Thibault

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - McGill

Collaborateur·rice de recherche - UdeM

Superviseur⋅e principal⋅e :

Doctorat - McGill

Sveta Zhuk

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Démasquer les deepfakes grâce à l'IA

Billets de blogue

Un groupe hétéroclite de huit jeunes adultes se tient serré sur un toit, souriant et riant avec la silhouette de la ville en arrière-plan. Deux encarts circulaires mettent en évidence une caméra vintage tenue par l'un des membres du groupe, soulignant l'élément deepfake.

16 décembre 2025

par

Victor Livernoche

Reihaneh Rabbany

Lire l'article

Flight-SEIR: Incorporating Flight Data to Improve Epidemiological Modelling and Disease Outbreak Prevention

3 août 2021

Flight-SEIR : incorporer les données de vol pour améliorer la modélisation épidémiologique et la prévention d’éclosions de maladies infectieuses

par

Shenyang Huang

Reihaneh Rabbany

Lire l'article

Publications

SandboxSocial: A Sandbox for Social Media Using Multimodal AI Agents

Maximilian Puelma Touzel

Sneheel Sarangi

Gayatri Krishnakumar

Busra Tugce Gurbuz

Austin Welch

Zachary Yang

Andreea Musulan

Hao Yu

Ethan Kosak-Hine

Tom Gibbs

Camille Thibault

Dan Zhao

The online information ecosystem enables influence campaigns of unprecedented scale and impact. We urgently need empirically grounded approa… (voir plus)ches to counter the growing threat of malicious campaigns, now amplified by generative AI. But, developing defenses in real-world settings is impractical. Social system simulations with agents modelled using Large Language Models (LLMs) are a promising alternative approach and a growing area of research. However, existing simulators lack features needed to capture the complex information-sharing dynamics of platform-based social networks. To bridge this gap, we present SandboxSocial, a new simulator that includes several key innovations, mainly: (1) a virtual social media platform (modelled as Mastodon and mirrored in an actual Mastodon server) that enables a realistic setting in which agents interact; (2) an adapter that uses real-world user data to create more grounded agents and social media content; and (3) multi-modal capabilities that enable our agents to interact using both text and images---just as humans do on social media. We make the simulator more useful to researchers by providing measurement and analysis tools that track simulation dynamics and compute evaluation metrics to compare experimental results.

2025-08-15

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (publié)

Veracity: An Open-Source AI Fact-Checking System

Taylor Lynn Curtis

Maximilian Puelma Touzel

William Garneau

Manon Gruaz

Mike Pinder

Li Wei Wang

Sukanya Krishna

Luda Cohen

The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper… (voir plus) introduces Veracity, an open-source AI system designed to empower individuals to combat misinformation through transparent and accessible fact-checking. Veracity leverages the synergy between Large Language Models (LLMs) and web retrieval agents to analyze user-submitted claims and provide grounded veracity assessments with intuitive explanations. Key features include multilingual support, numerical scoring of claim veracity, and an interactive interface inspired by familiar messaging applications. This paper will showcase Veracity's ability to not only detect misinformation but also explain its reasoning, fostering media literacy and promoting a more informed society.

2025-08-15

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (publié)

arxiv.org

Responsible AI Day

Ebrahim Bagheri

Faezeh Ensan

Calvin Hillis

Robin Cohen

Benjamin C. M. Fung

Sébastien Gambs

This special day event on Responsible Artificial Intelligence (AI) brings together researchers, practitioners, and policymakers to explore h… (voir plus)ow data mining and machine learning systems can be designed to align with ethical principles, societal values, and human well-being. As AI technologies increasingly influence decisions in healthcare, finance, governance, and social systems, there is a critical need to develop frameworks that embed fairness, accountability, and privacy directly into the foundations of knowledge discovery. This full-day event will feature a mix of invited talks, interactive debates, expert panels, and peer-reviewed research presentations, all focused on the practical integration of ethical design into data-driven systems. The Responsible AI Day builds on the success of Canada's NSERC CREATE Program on Responsible AI, an interdisciplinary initiative training the next generation of AI researchers across computer science, law, bioethics, public health, and media studies. Topics will span scalable AI governance, privacy-preserving computation, algorithmic bias mitigation, and the socio-legal tensions emerging in generative AI. By positioning responsible AI as a sociotechnical challenge, this special day aligns with KDD's mission of advancing data science that is not only technically robust but also socially conscious.

2025-08-02

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)

Temporal Graph Learning Workshop

Shenyang Huang

Daniele Zambon

Andrea Cini

Michael M. Bronstein

2025-08-02

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)

Uncovering Hidden Factions through Text-Network Representations: Unsupervised Public Opinion Mapping of Iran on Twitter in the 2022 Unrest

Sahar Omidi Shayegan

Ideological mapping on social media is typically framed as a supervised classification task that depends on stable party systems and abundan… (voir plus)t annotated data. These assumptions fail in contexts with weak political institutionalization, such as Iran. We recast ideology detection as a fully unsupervised mapping problem and introduce a text-network representation system, uncovering latent ideological factions on Persian Twitter during the 2022 Mahsa Amini protests. Using hundreds of millions of Persian tweets, we learn joint text–network embeddings by fine-tuning ParsBERT with a combined masked-language-modeling and contrastive objective and by passing the embeddings through a Graph Attention Network trained for link prediction on time-batched subgraphs. The pipeline integrates semantic and structural signals without observing labels. Density-based clustering reveals eight ideological blocs whose spatial relations mirror known political alliances. Alignment with 883 expert-labeled accounts yields 53% accuracy. This label-free framework scales to label-scarce contexts, offering new leverage for studying political debates online.

2025-07-25

colmweb.org/COLM/2025/Workshop/NLPOR (publié)

TRUTH: Teaching LLMs to Rerank for Truth in Misinformation Detection

Hao Yu

Shenyang Huang

Zachary Yang

Maximilian Puelma Touzel

2025-07-24

colmweb.org/COLM/2025/Workshop/SoLaR (poster)

A Systematic Literature Review of Large Language Model Applications in the Algebra Domain

Yajie Song

Yimei Zhang

Doina Precup

Maria Cutumisu

2025-06-09

Proceedings (International Conference of the Learning Sciences) (publié)

AIF-GEN: Open-Source Platform and Synthetic Dataset Suite for Reinforcement Learning on Large Language Models

Jacob Chmura

Shahrad Mohammadzadeh

Taz Scott-Talib

Nishanth Anand

2025-06-08

CODEML @ International Conference on Machine Learning (publié)

TGM: A Modular Framework for Machine Learning on Temporal Graphs

Michael M. Bronstein

Matthias Fey

While deep learning on static graphs has been revolutionized by standardized libraries like PyTorch Geometric and DGL, machine learning on T… (voir plus)emporal Graphs (TG), networks that evolve over time, lacks comparable software infrastructure. Existing TG libraries are limited in scope, focusing on a single method category or specific algorithms. We introduce Temporal Graph Modelling (TGM), a comprehensive framework for machine learning on temporal graphs to address this gap. Through a modular architecture, TGM is the first library to support both discrete and continuous-time TG methods and implements a wide range of TG methods. The TGM framework combines an intuitive front-end API with an optimized backend storage, enabling reproducible research and efficient experimentation at scale. Key features include graph-level optimizations for offline training and built-in performance profiling capabilities. Through extensive benchmarking on five real-world networks, TGM is up to 6 times faster than the widely used DyGLib library on TGN and TGAT models and up to 8 times faster than the UTG framework for converting edges into coarse-grained snapshots.

2025-06-08

ICML.cc/2025/Workshop/CODEML (publié)

Weak Supervision for Real World Graphs

Pratheeksha Nair

2025-05-31

arXiv (publié)

arxiv.org

From Intuition to Understanding: Using AI Peers to Overcome Physics Misconceptions

Ruben Weijers

Denton Wu

Hannah Betts

Tamara Jacod

Yuxiang Guan

Vidya Sujaya

Kushal Dev

Toshali Goel

William Delooze

Ying Wu

Generative AI has the potential to transform personalization and accessibility of education. However, it raises serious concerns about accur… (voir plus)acy and helping students become independent critical thinkers. In this study, we designed a helpful yet fallible AI "Peer" to help students correct fundamental physics misconceptions related to Newtonian mechanic concepts. In contrast to approaches that seek near-perfect accuracy to create an authoritative AI tutor or teacher, we directly inform students that this AI can answer up to 40\% of questions incorrectly. In a randomized controlled trial with 165 students, those who engaged in targeted dialogue with the AI Peer achieved post-test scores that were, on average, 10.5 percentage points higher—with over 20 percentage points higher normalized gain—than a control group that discussed physics history. Qualitative feedback indicated that 91% of the treatment group's AI interactions were rated as helpful. Furthermore, by comparing student performance on pre- and post-test questions about the same concept, along with experts' annotations of the AI interactions, we find initial evidence suggesting the improvement in performance does not depend on the correctness of the AI. With further research, the AI Peer paradigm described here could open new possibilities for how we learn, adapt to, and grow with AI.

2025-03-04

ICLR.cc/2025/Workshop/Bi-Align (poster)

Rethinking Anti-Misinformation AI

This paper takes a position on how anti-misinformation AI works should be developed for the online misinformation context. We observe that t… (voir plus)he current literature is dominated by works that produce more information for users to process and that this function faces various challenges in bringing meaningful effects to reality. We use anti-misinformation insights from other domains to suggest a redirection of the existing line of work and identify an under-explored opportunity AI can facilitate exploring.

2025-03-04

ICLR.cc/2025/Workshop/HAIC (publié)